Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Nov 25;26(1):363.
doi: 10.1186/s13054-022-04251-2.

Latent class analysis of imaging and clinical respiratory parameters from patients with COVID-19-related ARDS identifies recruitment subphenotypes

Daan F L Filippini  1 , Elisa Di Gennaro  2   3 , Rombout B E van Amstel  2 , Ludo F M Beenen  4 , Salvatore Grasso  5 , Luigi Pisani  6   7 , Lieuwe D J Bos  2   8   9 , Marry R Smit  2
Affiliations

Latent class analysis of imaging and clinical respiratory parameters from patients with COVID-19-related ARDS identifies recruitment subphenotypes

Daan F L Filippini et al. Crit Care. .

Abstract

Background: Patients with COVID-19-related acute respiratory distress syndrome (ARDS) require respiratory support with invasive mechanical ventilation and show varying responses to recruitment manoeuvres. In patients with ARDS not related to COVID-19, two pulmonary subphenotypes that differed in recruitability were identified using latent class analysis (LCA) of imaging and clinical respiratory parameters. We aimed to evaluate if similar subphenotypes are present in patients with COVID-19-related ARDS.

Methods: This is the retrospective analysis of mechanically ventilated patients with COVID-19-related ARDS who underwent CT scans at positive end-expiratory pressure of 10 cmH2O and after a recruitment manoeuvre at 20 cmH2O. LCA was applied to quantitative CT-derived parameters, clinical respiratory parameters, blood gas analysis and routine laboratory values before recruitment to identify subphenotypes.

Results: 99 patients were included. Using 12 variables, a two-class LCA model was identified as best fitting. Subphenotype 2 (recruitable) was characterized by a lower PaO2/FiO2, lower normally aerated lung volume and lower compliance as opposed to a higher non-aerated lung mass and higher mechanical power when compared to subphenotype 1 (non-recruitable). Patients with subphenotype 2 had more decrease in non-aerated lung mass in response to a standardized recruitment manoeuvre (p = 0.024) and were mechanically ventilated longer until successful extubation (adjusted SHR 0.46, 95% CI 0.23-0.91, p = 0.026), while no difference in survival was found (p = 0.814).

Conclusions: A recruitable and non-recruitable subphenotype were identified in patients with COVID-19-related ARDS. These findings are in line with previous studies in non-COVID-19-related ARDS and suggest that a combination of imaging and clinical respiratory parameters could facilitate the identification of recruitable lungs before the manoeuvre.

Keywords: ARDS; COVID-19; Latent class analysis; Mechanical ventilation; Phenotypes; Radiological data; Recruitment; Respiratory parameters.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Flowchart of the inclusion process
Fig. 2
Fig. 2
Profile plot of the two subphenotypes identified by the LCA. All variables used in the latent class analysis are plotted on the x-axis, with the y-axis displaying the standardized mean difference (SMD) of the corresponding variables in both of the LCA derived subphenotypes. SMDs are calculated by standardizing the variable to a mean of 0 and a standard deviation of 1. The variables on the x-axis are ordered by the y-value of the recruitable subphenotype in a descending way. TV Tidal volume, IBW ideal body weight, PEEP positive end-expiratory pressure
Fig. 3
Fig. 3
Non-aerated lung mass before and after the recruitment manoeuvre. Data are stratified and coloured by subphenotype. The y-axis shows non-aerated lung mass in grams, derived by quantitative CT analysis. Corresponding patient data points are connected by a line. The p values before and after recruitment (bottom p values) compare relative amounts (grams/total grams) by the recruitment manoeuvre and are derived by Wilcoxon signed-rank tests. The p value between subphenotypes (upper p value) compares the changes in relative amounts by the recruitment manoeuvre (change in grams/total grams) between subphenotypes and is derived by a Mann–Whitney U test (Additional file 1: Table S2)
Fig. 4
Fig. 4
Cumulative incidence function curves of ‘survival’ and ‘time until successful extubation’. Data are stratified and coloured by subphenotype. Time in days is displayed on the x-axis and the probability of an event on the y-axis. The annotated subdistribution hazard ratios (SHRs) compare successful extubation between subphenotypes in the presence of survival and are derived by means of a Fine and Gray competing risk analysis. Crude SHR and adjusted (age, gender and Apache II) SHR are presented, with the 95% confidence interval displayed between parentheses
See this image and copyright information in PMC

References

    1. Grasselli G, Zangrillo A, Zanella A, Antonelli M, Cabrini L, Castelli A, et al. Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the lombardy region. Italy Jama. 2020;323(16):1574–1581. doi: 10.1001/jama.2020.5394. - DOI - PMC - PubMed
    1. Ranieri VM, Rubenfeld GD, Thompson BT, Ferguson ND, Caldwell E, Fan E, et al. Acute respiratory distress syndrome: the Berlin Definition. JAMA. 2012;307(23):2526–2533. - PubMed
    1. Matthay MA, Zemans RL, Zimmerman GA, Arabi YM, Beitler JR, Mercat A, et al. Acute respiratory distress syndrome. Nat Rev Dis Primers. 2019;5(1):18. doi: 10.1038/s41572-019-0069-0. - DOI - PMC - PubMed
    1. Coppola S, Froio S, Marino A, Brioni M, Cesana BM, Cressoni M, et al. Respiratory mechanics, lung recruitability, and gas exchange in pulmonary and extrapulmonary acute respiratory distress syndrome. Crit Care Med. 2019;47(6):792–799. doi: 10.1097/CCM.0000000000003715. - DOI - PubMed
    1. Prescott HC, Calfee CS, Thompson BT, Angus DC, Liu VX. Toward smarter lumping and smarter splitting: rethinking strategies for sepsis and acute respiratory distress syndrome clinical trial design. Am J Respir Crit Care Med. 2016;194(2):147–155. doi: 10.1164/rccm.201512-2544CP. - DOI - PMC - PubMed

MeSH terms